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Research Article
Revised

Validating instructional practice scale for university instructors in Ethiopia

[version 2; peer review: 3 approved with reservations, 1 not approved]
PUBLISHED 07 May 2025
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Abstract

Background

Measurement is essential for methods of instruction to be successful. Having an instrument that is reliable and validated in the given setting is vital. The primary goal of this research project is to validate the instructional practice scale (IPS) for university instructors in the Ethiopian context.

Methods

By implementing a cross-sectional descriptive survey research design, 1,254 participants across four public universities – Arbaminch, Dilla, Wachamo, and Jinka representing the first, second, third, and fourth generations, respectively were randomly selected and participated. The data was split in half and underwent an exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

Results

The three components of the EFA were alternately filled with seventeen items that satisfied certain standards, had a loading value of > .5, and a Cronbach alpha of ≥ .874. The factors identified in the EFA have been confirmed to be the thirteen items with loading, Cronbach alpha, Raykov’s rho coefficient (rho_A), composite reliability (CR) value of >.7, and Average Variance Explained (AVE) value of > .5. Tests of measurement and structural models showed a good fit. The Fornell-Larcker criterion, which is employed in discriminant validity analysis, demonstrates that the square root of the AVE for each construct is higher than the correlation it exhibits with other constructs. The correlations’ heterotrait–monotrait (HTMT) ratio is getting close to zero, and there isn’t one in the confidence interval at the.05 significance level. Both guaranteed strong discriminant validity.

Conclusion

For university instructors, the 13 items generally have powerful psychometric properties. The three subsections of the instruction practice scale—planning (4 indicators), delivering (4 indicators), and assessment (5 indicators) — are meant to measure the instructional practice effectively. Implications of the findings were further discussed.

Keywords

Instructional practice, public university instructors, validation, factor analysis

Revised Amendments from Version 1

Changes have been made in response to the reviewer's comments in this revised manuscript, with particular emphasis on the introduction, limitations, and conclusion sections.

See the authors' detailed response to the review by Kris Stutchbury

1. Introduction

One of the most important factors in encouraging students to learn and succeed better is the instructors’ instructional practice. In a transmission model of teaching, a teacher imparts knowledge and students absorb it passively (Emanalia, 2017). Traditionally, instructional practice—also referred to as teacher-centred practice—is a formal and controlled instructional practice where the instructor plans what, when, and how students learn (Horvat-Samardžija, 2011); teachers were the ones who created the lesson in the classroom (Saleh & Jing, 2020). An alternative view of instructional practice highlights the needs and viewpoints of pupils. How, what, and when learning occurs is set by both the instructor and the pupils (Horvat-Samardžija, 2011). Schweisfurth (2013) posits that learner-centered education (LCE) is fundamentally rooted in pedagogical attitudes rather than mere practices, outlining key dimensions such as classroom relationships (ranging from authoritarian to democratic), learner motivation (extrinsic versus intrinsic), the nature of knowledge (fixed versus fluid), the teacher’s role (authoritative versus facilitative), and curriculum design (fixed versus negotiated). Within this framework, effective LCE implementation includes practices such as leveraging sociodemographic data—including learners’ cultural backgrounds, family structures, and community contexts—to tailor instruction, as well as fostering an inclusive learning environment where individual differences are respected to cultivate a sense of belonging. Such principles can be operationalized through strategies like allowing students to select assignment topics aligned with their interests, collaboratively establishing classroom norms to promote mutual respect, integrating culturally responsive materials (e.g., texts, case studies, and examples reflective of student diversity), and employing differentiated instruction that accommodates varied learning modalities (e.g., visual, auditory, and kinesthetic). These approaches collectively ensure that pedagogical practices align with the ethos of LCE, promoting equity, engagement, and academic success. Similarly, this examines instructional practices through the lens of how educational institutions organize (plan), implement (deliver), and evaluate (assess) student learning, while integrating learners’ perspectives—with a particular emphasis on the behavioral dimension of instructional practices as implied in Bibon (2022).

Research indicates that teachers, a lack of course materials, students’ disinterest in the subject, and ineffective teaching strategies are all important factors influencing students’ performance (Majo, 2016). In fact, significant funds are allocated to enhancing institutions and developing educational materials to augment students’ performance (Barrett, 2018). Nonetheless, there have been initiatives to deliver education; Open University (2018) and Iglesias (2016), for instance, modified the science curriculum to provide instruction that enhances student learning. This generally suggests that instrument validation intended to evaluate one of the essential components of success—that is, instructional practice—is either not prioritized or is not given enough weight. Competency-based assessments utilizing instruments that have been contextually validated have lagged despite the amplification of the instruction issue.

Many academics evaluate teaching or instructional practices from diverse angles. Learner-centred teaching practices, such as those found in Sarwar, Zerpa, Hachey, Simon, & Barneveld (2012); classroom organization, student orientation, and enhanced activities-based measures that were adapted from the Organization for Economic Co-operation and Development (OECD) (2009); high school instructional practice measures that emphasize a focus on people (Fischer, Fishman, Dede, Eisenkraft, Frumin, Foster,… McCoy, 2018); performance criteria-based measures (Zemelman, Daniels & Hyde, 2005); teaching for conceptual understanding measures (Mullis, Martin, Gonzalez, Gregory, Garden, O’Connor, & Smith, 2000); and observations (Saleh & Jing, 2020). Neither of them speaks of every stage of instructional practice, from preparation to evaluation. By assessment, Bibon (2022) addresses these kinds of problems. Collecting the items from Abundo (2019), Benosa (2018), and Sergio (2018) into components related to instructional planning, delivery, and assessment, Bibon (2022) addresses these kinds of problems.

In general, we validated the instructional practice scale to more accurately assess the construct in the context of Ethiopian university instructors, presuming that the Ministry of Education would provide freshman students with uniform learning modules. Measuring this construct using a validated instrument is essential to gaining knowledge of it, effectively conveying that to others, and making necessary corrections. A construct needs to be evaluated using a reliable and appropriate in-context tool to obtain its images. In light of this, the following goals of the study were set:

  • Investigate the fundamental factor structures in instructional practice in EFA.

  • Verify the structures discovered using exploratory factor analysis.

  • Assess the tool’s psychometric qualities, such as validity and reliability, in the setting of Ethiopian public university instructors.

2. Methods

2.1 Study design, setting, and the instrument

The purpose of this study was to validate the instructional practice tool in the context of Ethiopian public university instructors. Hence, we employed a cross-sectional descriptive survey method to gather data from the target population at certain points in time. We randomly select the southern part of the country. All eight universities identified in it, are categorized based on generation (year of establishment). Four public universities—Arbaminch, Dilla, Wachamo, and Jinka—representing the first, second, third, and fourth generations, respectively, were utilized to select participants.

The items were first developed by Abundo (2019), Benosa (2018), and Sergio (2018) to account for the instructional practice of teachers through classroom observations to compile their thesis at Bicol University. By 2022, Bibon extracted those items and compiled them in the form of a scale responded in five alternative responses - never, rare, sometimes, frequently, and always. Grounding on the constructivism of teaching and learning and the suggestion of educational institutions, Bibon (2022) classified into three categories of instructional practice. These include—planning (8 indicators), delivering (9 indicators), and assessing (8 indicators)—the scale is meant to measure the instructions used by scientific teachers. In his study, the scale’s Cronbach alpha of .86 indicated better internal consistency when assessing the construct.

2.2 Population (or participants and sampling)

The study’s target population consisted of instructors at Arbaminch, Dilla, Wachamo, and Jinka universities. Various sample sizes have been suggested to perform factor analysis. For instance, the following criteria are deemed excellent: at least ten times as many subjects as variables (Everitt, 1975; Nunnally, 1978); at least 100 subjects (Gorsuch, 1983; Kline, 1994); sample size to the number of variables (e.g., three to six subjects per variable) (Cattell, 1978); sample size-to-parameter ratio of 20:1 (Jackson, 2003); and 50 - Very poor, 100 - poor, 200 - fair, 300 - good, 500 - very good, and 1,000 or more scale of sample adequacy are excellent (Comrey & Lee, 1992). According to Comrey and Lee, a total of 1300 individuals were chosen to detect structures, representing an excellent sample size (1000) accounting for a maximum response error of 30%.

Kothari’s (2004) stratified proportional sample size formula, nh = (Nh/N)*n, was employed to draw participants proportionally from the four universities. Where N represents the entire population size, Nh represents the sample size for the hth stratum, nh represents the sample size, and n is the sample size. Therefore, nh calculated as follows: 432 for Arbaminch University out of 1,720 instructors, 318 for Dilla University out of 1,263 instructors, 281 for Wachamo out of 1,119 instructors, and 269 for Jinka University out of 1,069 instructors, assuming N = 5,171 and n = 1300.

Since they were either inadequately completed, incomplete, or not returned, 46 response papers were removed. The 1,254-participant data were randomly divided into two groups, 627 participants in each group. The data was then utilized to find patterns of structure and confirm them using confirmatory factor analysis (627).

2.3 Procedures

2.3.1 Content validity evaluation

According to Almohanna, Win, Meedya, and Vlahu-Gjorgievska (2022), reliable instruments yield reliable data. Lawshe’s (1975) content validity quantitative evaluation method was used to evaluate each item by nine experienced subject matter experts (SMEs) from the following fields: social psychology, educational planning and management, curriculum and instructional provision, educational measurement and evaluation, and so on. The formula for computation is displayed as follows:

CVR=(neN/2)/(N/2)

CVR = content validity ratio

ne = number of panellists pointing to the item as ‘essential’

N = total number of panellists

A three-point rating system was used to rank each item on the draft data-gathering tool (1 not essential, 2 useful but not essential, and 3 essential). CVR has a value between -1 and +1. The item is deemed acceptable and clear if the value is positive; it should be reworded, modified, or rejected if the value is negative; and it is deemed necessary and legitimate if 50% of the panellists in the N size assess the item as essential. In general, every item satisfies the acceptable standard of ≥.75 (Lawshe, 1975), suggesting that the items are extremely important. The overall mean of all items in the scale using the Content Validity Index (CVI) statistical technique was .88, exceeding ≥.70 standard given by Tilden, Nelson, and May (1990), and ≥.8 suggested by Davis (1992).

Table 1. Instructors’ instructional practice tool content validity ratings.

ItemPanelistsCVR Decision
12345678 9
1333333323.77Appropriate
23333333331Appropriate
33333333331Appropriate
4333233333.77Appropriate
53333333331Appropriate
63333333331Appropriate
7333233333.77Appropriate
83333333331Appropriate
9333333323.77Appropriate
103333333331Appropriate
11333333332.77Appropriate
12333332333.77Appropriate
133333333331Appropriate
143333333331Appropriate
15333333323.77Appropriate
16233333333.77Appropriate
173333333331Appropriate
18333333323.77Appropriate
193333333331Appropriate
20323333333.77Appropriate
213333333331Appropriate
223333333331Appropriate
23333233333.77Appropriate
24332333333.77Appropriate
25333332333.77Appropriate
S-CVI/Ave.92.92.92.761.841.68.92.88 Appropriate

2.3.2 Data collection

This study was conducted in 2022 to 2023/24 academic year. The participants were recruited between May 1st and May 30th, 2023, and data was gathered from June 1st to June 30th, 2023. The questionnaire was administered in person. We gathered an endorsement letter and reached out to department heads and deans of colleges and institutes. They were given a brief overview of the study’s objectives, the possible participants, the type of data collection tool, and the typical amount of time needed to complete the questionnaire. Through establishing a communication channel with these high-ranking officials at various stages, a survey was dispersed around departmental offices. After that, it was distributed at random among instructors who provided consent until the target number of participants was attained.

2.3.3 Data analysis

Cleansing of data was done before data analysis. As a result, eight response sheets that were improperly filled out and 3 that were not returned were excluded. SPSS-23 (https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-23) and a free trial version smartPLS-4 (https://www.smartpls.com/downloads/) were utilized for the data analysis. SmartPLS was used to investigate confirmation factor analysis, and descriptive statistics and exploratory factor analysis were carried out using SPSS.

2.4 Ethics and consent

The Center for Educational Research along with the Office of Research and Dissemination and the Office of Vice President for Research and Technology Transfer at Dilla University have ensured that the issue under investigation complies with academic research criteria and ethical standards on 13/01/2023.

Potential participants received brief instructions regarding the study’s overall goal and the characteristics of the data collection instrument. The representatives from the above-mentioned offices on the same date confirmed that collecting oral consent from participants is sufficient for the present study. Accordingly, we obtained verbal informed consent from each participant. The confidentiality of the participants was greatly protected by avoiding mentioning their names and other relevant identifiers during the data collecting and reporting procedure. Representatives from Center for Educational Research, Office of Research and Dissemination, and the Office of Vice President for Research and Technology Transfer at Dilla University were approved the unharmfull nature of the data collection tool and assumed the number of participants and confirmed that collecting verbal consent from participants is sufficient for the present study.

3. Result and discussion

3.1 Socio-demographic characteristics

Table 2 indicates that 1,254 instructors took part. Approximately 956 (76.2%) participants were male, and 298 (23.8 %) participants were female, making up around three-fourths and one-fourth of the total, respectively. The age distribution has a mean of 34.16 years and a standard deviation of 4.37, falling between the minimum age of 28 and the maximum age of 45. This number appears to be in line with the distributions of work experiences and academic ranks. There were 1186 (94.6%) master’s degree holders, 50 (4%) PhD holders and 18 (1.4%) assistant lecturers as the final minimum size. This means that the instructors in the minimum, maximum, and average age groups will be covered, accordingly. One year of work experience at a university is the minimum, while sixteen years is the maximum. Ultimately, 852 (67.9 %) participants, or the higher two-thirds, underwent training for higher education teaching under the Higher Diploma Program (HDP). In contrast, approximately one-fourth of instructors were not.

Table 2. Socio-demographic characteristics of participants (N = 1,254).

VariablesAttribute Frequency (%)
SexMale956 (76.2)
Female298 (23.8)
Age (years)Min.28
Mean (SD)34.16 (4.37)
Max.45
Academic rankAssistant lecturer18 (1.4)
Lecturer1186 (94.6)
PhD50 (4)
Work experience (university, years)Min.1
Mean (SD)5.11 (3.06)
Max.16
Higher Diploma ProgramYes852 (67.9)
No402 (32.1)

3.2 Exploratory Factor Analysis (EFA)

We employed the Direct Oblimin with Kaiser Normalization Rotation Method, a Maximum Likelihood (ML) Extraction Method, an Eigenvalue exceeding one, and a factor loading cut-off value of .5 (greater than the default criteria, i.e., .3). By assuming that the observed variables are normally distributed, ML produces factor structures with high correlations of indicators. With big sample sizes, ML yields estimates that are effective, less skewed, and less variable.

3.2.1 Assumption test result

To move forward with EFA, multiple assumptions were examined. According to George and Mallery (2019) and Hair, Hult, Ringle, and Sarstedt (2022), the data distribution resulted in a relatively normal distribution, falling within the range of ±1, -.394 for skewness, and 0.014 for kurtosis. Other tests, such as the Kolmogorov-Smirnova, Shapiro-Wilk, and Z values or critical ratios for normalcy, have shown minor violations (skewed to negative).

Variance Inflation Factor (VIF) results for instructional planning, delivery, and assessment were 1.052, 1.049, and 1.004, respectively. Tolerance values were.95 for instructional planning, .954 for instructional delivery, and .996 for instructional assessment sub-scales. As indicated in the literature, both tests verified that there was no issue with multicollinearity with this set of data. for example, a VIF higher than 5 to 10 (Kim, 2019), VIF greater than 10 and a tolerance value < 0.10 (Hair, Black, Babin, & Anderson, 2010) indicates a potential problem of multicollinearity, a VIF < 5 (Ringle, Da Silva, & Bido, 2014; Rogerson, 2001) and even 4 (Pan & Jackson, 2008) are considered acceptable.

The internal consistency of the overall and subscale items was checked using Cronbach alpha, resulting in .874, .928, .886, .786 and instructional planning, delivery, assessment subscales and overall scale, respectively ( Table 4). As stated by Sarstedt (2019), this guarantees the measurement’s unidimensionality and sub-dimensionality nature and satisfies the need for EFA analysis (.7 minimum criteria). The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is .846, which is around the desired (≥.70) category, according to Kaiser (1974), Hoelzle & Meyer (2013), and Lloret, Ferreres, Hernandez, & Tomas (2017). Bartlett’s Test of Sphericity, 7921.264 (p=.00), further confirms that the data are suitable for EFA analysis ( Table 3).

Table 3. KMO and Bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.818
Bartlett’s Test of SphericityApprox. Chi-Square 7921.264
df300
Sig..000

3.2.2 Exploratory factor analysis result

Six factors were obtained by rotating the matrix. But there wasn’t a single item loaded in the sixth factor. In the fourth factor, only two items (items 5 and 6) and in the fifth factor, only one item (item 16) were loaded. Therefore, the last three factors were eliminated since they did not meet the criteria for having three to five items in each component and were not appropriate for conducting confirmatory factor analysis, as stated by (MacCallum, Widaman, Zhang, & Hong, 1999; Raubenheimer, 2004). Additionally, there was no loading in any factor for items 8, 13, 14, 15, and 17. This means that items were loaded below the given threshold (.50). Eight items were eliminated overall.

Table 4 illustrates the rotation of eight items to the instructional assessment (IA) subscale (loading .573 to .821), four items to the instructional delivery (ID) subscale (loading .854 to .892), and five items to the instructional planning (IP) subscale (loading .569 to .874). For IA, ID, IP, and overall scale, the internal consistency or reliability values of .886, .928, .874, and .806 exhibit robust (Taber, 2018) and good dependability (Salkind, 2015; Tavakol and Dennick, 2011; Lavrakas, 2008).

Table 4. Summary of descriptive statistics, rotated factor matrix, and alpha value of instructional practice (N = 627).

FactorItem codeItemsLoadingUniqnessAlpha
Instructional Assessment (8 items)IA21Uses multiple assessment methods, including adjusted pacing and flexible grouping, to engage learners in active learning opportunities that promote the development of critical and creative thinking, problem-solving, and performance capabilities.821.344.886
IA25Creates assessment method that is sustainable and with continuity to trace behavioral and cognitive changes of learners through time.768.435
IA24Uses learning materials like module, activity sheets, SIM etc. that evaluates learning inside and outside the school.749.410
IA19Provides opportunities for the development of performance-based assessment.743.401
IA23Provides assessment that allows learners to work individually or in groups through independent/cooperative learning.701.495
IA22Provides multiple assessment strategies for the differentiation and accommodation of individual differences.664.499
IA20Shows relevance and connection between topic discussed vis-à-vis assessment strategy.601.629
IA18Provides opportunities for the development of product-based assessment.573.532
Instructional Delivery (4 items)ID11Facilitates a learning environment where sense of belonging of learners through individual differences is respected.892.205.928
ID10Connects prior knowledge of the learners to the new information of the lesson.871.226
ID9Discusses lessons in increasing levels of complexity and difficulty.870.234
ID12Uses varying perspectives, theories and methods of investigation and inquiry in instructing the concept of the lesson.854.266
Instructional Planning (5 items)IP4Creates and plans strategies that allow multiple learning areas to be integrated in the lesson.874.207.874
IP2Assesses teaching materials for its relevance to the learning competency attainment and needs of learners..872.232
IP1Uses and analyzes information of learners to design instruction that meets the diverse needs of learners and leads to ongoing growth and achievement.749.429
IP3Uses present data of learners to design instruction that is differentiated on the individual learning needs of learners..738.458
IP7Uses sociodemographic information regarding learners’ background like culture, family structure and status, and communities in planning instruction suited to the needs of the learners.569.634

According to the total variance explained analysis, the three components collectively account for 60.95% of the variance in instructional practice. This demonstrates that irrespective of rotation methods and disciplines, 50% explained variance is sufficient (Sürücü, Şeşen, & Maslakçı, 2021; Beavers, Lounsbury, Richards, Huck, Skolits, & Esquivel, 2013; Hair, Sarstedt, Pieper, & Ringle, 2012; Pett, Lackey, & Sullivan, 2003). Furthermore, instructional delivery and instructional planning variables accounted for roughly comparable variance (18.51% and 18.47%), respectively. Whereas the instructional evaluation factor explains the relatively highest share of variance (23.97%) ( Table 5).

Table 5. Total variance explained.

FactorInitial eigenvaluesExtraction sums of squared loadingsRotation sums of squared loadings
Total% of varianceCumulative %Total% of varianceCumulative %Total% of varianceCumulative %
14.67827.51827.5183.32619.56319.5634.07523.97323.973
23.67221.59749.1163.79622.33241.8953.14718.51342.486
33.07018.05967.1743.24019.06060.9553.14018.47060.955

When the sample size is 200 or higher, Cattell’s Scree Plot test is still another trustworthy method to ascertain the number of components (Sürücü, Yikilmaz, & Maslakci, 2022). Starting with the fourth component indicates a notable linear trend in the eigenvalue pattern ( Figure 1). We reasonably retained the three factors at 60.95%.

563e1f18-4db0-4e83-a862-380475ee6adc_figure1.gif

Figure 1. Scree plot factor.

Furthermore, we conducted parallel analysis to confirm wether the number of components in EFA loading and scree plot are actual factors or due to chance. Six hundred twenty seven cases, 25 items, 95% specifications, and principal components analysis method were specified. As a result, only the first three rawdata egenvalues are greater than the respective prcntyle of the random data egenvalues ( Figure 2). The parallel analysis confitrmed that three components are extracted in EFA as suggested by (O’Connor, 2000).

563e1f18-4db0-4e83-a862-380475ee6adc_figure2.gif

Figure 2. Parallel analysis plot.

3.3 Confirmatory Factor Analysis (CFA) Result

3.3.1 Common Method Bias (CMB)

CMB analysis is advised as the data samples were obtained by a questionnaire and/or all variables were obtained from the same individuals (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). As a result, we used Harman’s single-factor test to assess CMB, and the results showed that it explains 24.284% of the variation, which is lower than the 50% acceptable limit.

3.3.2 Measurement Model

Convergent validity, internal consistency reliability, and discriminant validity were examined using a reflective measurement approach. The extent to which a component is positively correlated with another factor that assesses the same construct is known as convergent validity. Factor loadings and average variance extraction were used for testing it. As a result, Items 22, 20, 23, and 25 in the instructional assessment factor and Item 1 in the instructional planning factor were loaded .398, .582, .606, .694, and .698 respectively ( Figure 3).

563e1f18-4db0-4e83-a862-380475ee6adc_figure3.gif

Figure 3. Outer loading and AVE values before modification.

According to Hair, Hult, Ringle, and Sarstedt (2016), Henseler, Ringle, & Sarstedt (2014), and Hair, Black, and Babin (2010), this indicates below the threshold (≥.7). Following a sequential removal and reanalysis of the first three relatively low-loaded items (items 22, 20, and 23), item 25 improved from .694 to .723, satisfying the threshold. As a result, according to Sarstedt, Ringle, and Hair et al. (2017), Henseler et al. (2014), and Hair et al. (2010), the Average Variance Explained (AVE) in instructional assessment also improved from .476 ( Figure 3) to .607 ( Figure 4), met the minimum acceptable criterion (>.5). To satisfy the minimally acceptable standards of item loading values and AVE values, four items—three from the instructional assessment and one from the instructional planning—were generally removed.

563e1f18-4db0-4e83-a862-380475ee6adc_figure4.gif

Figure 4. Outer loading and AVE values after modification.

Raykov’s rho coefficient, composite reliability (CR), and Cronbach alpha were employed to assess the construct validity and reliability ( Table 6). As per (Hair, Hult, Ringle, & Sarstedt, 2017; Hair, Black, Babin, & Anderson, 2010), the outcome satisfies the acceptable criterion for all (.7 to .95).

Table 6. Construct reliability and validity test.

Constructs n itemsCronbach alpha (>.7)rho_A (>.7)CR (>.7) AVE (>.5)
IA50.8611.0460.8840.607
ID40.9280.9370.9490.822
IP40.8490.8910.8950.681

The degree of differentiation between a component and another component is known as discriminant validity. The results of the tests on the heterotrait–monotrait (HTMT) ratio of correlations between constructs and the Fornell-Larcker criterion are displayed in Table 7 and fall within an acceptable range. According to Hair, Risher, Sarstedt, & Ringle (2019); Henseler, Ringle, & Sarstedt (2014); Fornell, Larcker (1981), this indicates that each construct’s square root of the AVE (all bold crossing values) in the Fornell-Larcker criterion exceeds its intercorrelations with other constructs or greater than the absolute measure of any correlation. Henseler et al. (2014) criticized the Fornell and Larcker test for not consistently detecting the absence of discriminant validity in some study scenarios.

Table 7. Discriminant validity.

Fornell-and-Larcker testHeterotrat-Monotrait Ratio (HTMT)
IAIDIPIAID IP
IA0.779 IA
ID0.1490.907 ID0.137
IP0.3100.0870.825 IP0.2420.100

In order to evaluate discriminant validity, we thus looked at an alternative set of HTMT criteria. The results indicate that there is no discriminant validity issue, according to Hair et al. (2019) and Henseler et al. (2014). Because there is a positive correlation between IP and IA (r = .242, p =.00), IP and ID (r = .1, p =.00), and IA and ID (r = .137, p =.000). The ratio of correlations is also getting closer to zero. The two-tailed confidence interval at the.05 significance level does not include one.

3.3.3 Structural model

We checked the estimated model’s goodness-of-fit. There was a .08 marginal standard root means square residual (SRMR). Based on Brown’s (2015) analysis, the model is appropriate if ≤ 0.08. We also used the variance inflation factor (VIF) to assess for multicollinearity amongst the latent components. Multicollinearity problems are indicated by a VIF of greater than five (Hair, Risher, Sarstedt, & Ringle, 2019; Sarstedt, Ringle, Hair, 2017). Since all of the numbers in Table 8 are ≤ 3.529, multicollinearity is not an issue for the model.

Table 8. Items loading, mean, standard deviation, and variance inflation.

ConstructsIndicatorsLoadingMeanSd VIF (≤5)
IAIA180.8732.8241.1231.580
IA190.7672.4781.0711.730
IA210.7742.4921.1032.644
IA240.7532.6251.0491.844
IA250.7232.6391.0812.368
IDID100.9183.5771.0363.327
ID110.9073.5551.0073.529
ID120.8953.5581.0053.017
ID90.9063.5071.0363.179
IPIP20.8463.4491.0052.726
IP30.7643.4651.0291.980
IP40.8953.3971.0332.672
IP70.7903.3441.0181.445

4. Limitations

A key limitation of this study lies in its dependence on self-reported data, which may introduce response biases, including social desirability bias and variations in participants’ perceptions, understanding, and interpretations of instructional practices. To mitigate potential misunderstandings, clear explanations of instructional practice definitions, survey items, and completion guidelines were provided to participants prior to data collection. To strengthen validity, subsequent research should incorporate multi-method assessments, including classroom observations and peer evaluations, to triangulate findings. Additionally, longitudinal studies could assess the instrument’s predictive validity in relation to student learning outcomes. To assure the true representations of instructors underlying beliefs, a random 2-5 class students were checked to see if their instructors allowed them to select assignment topics related to their interests, facilitated a discussion where students suggested norms and agreed on respectful behavior standards, incorporated texts, examples, and case studies that reflected students’ diverse cultural backgrounds, and considered varying learning styles (e.g., visual, auditory, kinesthetic) in lesson delivery. Generally, by addressing these limitations, future work can further refine the tool’s applicability across diverse educational settings while advancing methodological rigor in pedagogical research.

5. Conclusion and future research directions

The validation of the instructional practice scale for university instructors underscores the critical importance of context-specific instrument development. Through EFA, the initial 25-item scale was refined to 17 items across three key dimensions, with CFA further validating a final 13-item model. This study provides a psychometrically robust tool for assessing instructional practices at the university level, demonstrating its applicability for research, professional development, and promotion-related evaluations. However, its generalizability is currently limited to broad university-level courses, necessitating further discipline-specific adaptations to account for pedagogical variations across academic fields.

The findings carry significant implications for both academic research and instructional practice. First, the validated instrument offers a reliable means for evaluating and enhancing instructional effectiveness in higher education, supporting evidence-based faculty development initiatives. Second, the study highlights the necessity of contextual validation, suggesting that future researchers should develop and test specialized instruments tailored to distinct disciplines to ensure pedagogical relevance. Generally, this serves as a valuable foundation for discussing professional development needs, rather than representing a definitive assessment of competence.

Ethics and consent

The Center for Educational Research along with the Office of Research and Dissemination and the Office of Vice President for Research and Technology Transfer at Dilla University have ensured that the issue under investigation complies with academic research criteria and ethical standards on 13/01/2023 (DU/164/2023). Potential participants received brief instructions regarding the study’s overall goal and the characteristics of the data collection instrument. The representatives from the above-mentioned offices on the same date confirmed that collecting oral consent from participants is sufficient for the present study. When requested, participants preferred to give their consent orally rather than in writing. They perceived written consent as requiring a lot of resources, including time. Accordingly, we obtained verbal informed consent from each participant. Representatives from Center for Educational Research, Office of Research and Dissemination, and the Office of Vice President for Research and Technology Transfer at Dilla University approved the unharmful nature of the data collection tool and assumed the number of participants and confirmed that collecting verbal consent from participants is sufficient for the present study.

Comments on this article Comments (1)

Version 2
VERSION 2 PUBLISHED 07 May 2025
Revised
  • Reviewer Response 09 Aug 2025
    Anh Khau, Tra Vinh University, Trà Vinh, Vietnam
    09 Aug 2025
    Reviewer Response
    Thank you for giving me this chance to examine this work.
    First, regarding questionnaire validation, the authors underwent a detailed procedure, so the result can be reliable. 
    Second, I have ... Continue reading
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Mehari A, Kassahun B, Berhanu H et al. Validating instructional practice scale for university instructors in Ethiopia [version 2; peer review: 3 approved with reservations, 1 not approved]. F1000Research 2025, 13:975 (https://doi.org/10.12688/f1000research.152815.2)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Current Reviewer Status: ?
Key to Reviewer Statuses VIEW
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 2
VERSION 2
PUBLISHED 07 May 2025
Revised
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10
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Reviewer Report 05 Jun 2025
Anh Hoang Khau, Tra Vinh University, Trà Vinh, Tra Vinh, Vietnam 
Approved with Reservations
VIEWS 10
Thank you for giving me this chance. I appreciate how hard work the authors have put in. They, actually, went through meticulous analysis. 
However, I have some suggestions. The authors can refer to some more updated publications about the ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Khau AH. Reviewer Report For: Validating instructional practice scale for university instructors in Ethiopia [version 2; peer review: 3 approved with reservations, 1 not approved]. F1000Research 2025, 13:975 (https://doi.org/10.5256/f1000research.181205.r385286)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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2
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Reviewer Report 03 Jun 2025
Wendy M Smith, Center for Science, Mathematics & Computer Education and Department of Mathematics, University of Nebraska-Lincoln, Lincoln, USA 
Not Approved
VIEWS 2
It is very important to take existing instruments and investigate specific uses and score interpretations. Thus, this type of study is important, to understand how Ethiopian instructors respond to the items. The authors had expert review of items on ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Smith WM. Reviewer Report For: Validating instructional practice scale for university instructors in Ethiopia [version 2; peer review: 3 approved with reservations, 1 not approved]. F1000Research 2025, 13:975 (https://doi.org/10.5256/f1000research.181205.r383624)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
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5
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Reviewer Report 02 Jun 2025
Fahad A. Salendab, Sultan Kudarat State University, Tacurong City, Region XII, Philippines 
Approved with Reservations
VIEWS 5
1. Provide a further description in analyzing the data. It is good if the authors would mention specific statistical analyses that are appropriate to this study.
2. In-text citations must be updated since most citations are outdated. it must ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Salendab FA. Reviewer Report For: Validating instructional practice scale for university instructors in Ethiopia [version 2; peer review: 3 approved with reservations, 1 not approved]. F1000Research 2025, 13:975 (https://doi.org/10.5256/f1000research.181205.r383621)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
Version 1
VERSION 1
PUBLISHED 28 Aug 2024
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20
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Reviewer Report 26 Apr 2025
Kris Stutchbury, Faculty of Wellbeing, Education and Language Studies, The Open University, Milton Keynes, England, UK 
Approved with Reservations
VIEWS 20
Firstly I must stress that my knowledge of statistics is insufficient to judge the quality of the statistically analysis, although the number of responses obtained is impressive. However, I do understand pedagogy and am interested in an instrument that claims ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Stutchbury K. Reviewer Report For: Validating instructional practice scale for university instructors in Ethiopia [version 2; peer review: 3 approved with reservations, 1 not approved]. F1000Research 2025, 13:975 (https://doi.org/10.5256/f1000research.167618.r373620)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 09 May 2025
    Anemut Mehari, Dilla University, Dilla, Ethiopia
    09 May 2025
    Author Response
    Responses to reviewer's comments 
    Dear Reviewer,
    We greatly appreciate your insightful and constructive feedback. The revised manuscript has been amended to address all of the points you raised, as detailed ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 09 May 2025
    Anemut Mehari, Dilla University, Dilla, Ethiopia
    09 May 2025
    Author Response
    Responses to reviewer's comments 
    Dear Reviewer,
    We greatly appreciate your insightful and constructive feedback. The revised manuscript has been amended to address all of the points you raised, as detailed ... Continue reading

Comments on this article Comments (1)

Version 2
VERSION 2 PUBLISHED 07 May 2025
Revised
  • Reviewer Response 09 Aug 2025
    Anh Khau, Tra Vinh University, Trà Vinh, Vietnam
    09 Aug 2025
    Reviewer Response
    Thank you for giving me this chance to examine this work.
    First, regarding questionnaire validation, the authors underwent a detailed procedure, so the result can be reliable. 
    Second, I have ... Continue reading
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
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